Gen4D: Synthesizing Humans and Scenes in the Wild

• URL: http://arxiv.org/abs/2506.05397v1
• Date: Tue, 03 Jun 2025 20:04:41 GMT
• Title: Gen4D: Synthesizing Humans and Scenes in the Wild
• Authors: Jerrin Bright, Zhibo Wang, Yuhao Chen, Sirisha Rambhatla, John Zelek, David Clausi,
• Abstract summary: We introduce Gen4D, a fully automated pipeline for generating diverse and photorealistic 4D human animations.<n>We present SportPAL, a large-scale synthetic dataset spanning three sports: baseball, icehockey, and soccer.<n>Gen4D and SportPAL provide a scalable foundation for constructing synthetic datasets tailored to in-the-wild human-centric vision tasks.
• Score: 10.696169692478275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lack of input data for in-the-wild activities often results in low performance across various computer vision tasks. This challenge is particularly pronounced in uncommon human-centric domains like sports, where real-world data collection is complex and impractical. While synthetic datasets offer a promising alternative, existing approaches typically suffer from limited diversity in human appearance, motion, and scene composition due to their reliance on rigid asset libraries and hand-crafted rendering pipelines. To address this, we introduce Gen4D, a fully automated pipeline for generating diverse and photorealistic 4D human animations. Gen4D integrates expert-driven motion encoding, prompt-guided avatar generation using diffusion-based Gaussian splatting, and human-aware background synthesis to produce highly varied and lifelike human sequences. Based on Gen4D, we present SportPAL, a large-scale synthetic dataset spanning three sports: baseball, icehockey, and soccer. Together, Gen4D and SportPAL provide a scalable foundation for constructing synthetic datasets tailored to in-the-wild human-centric vision tasks, with no need for manual 3D modeling or scene design.

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